Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a specimen. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then separated into individual semiconductor devices.
Optical metrology processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield. Optical metrology techniques offer the potential for high throughput without the risk of sample destruction. A number of optical metrology based techniques including scatterometry and reflectometry implementations and associated analysis algorithms are commonly used to characterize critical dimensions, film thicknesses, composition and other parameters of nanoscale structures.
As devices (e.g., logic and memory devices) move toward smaller nanometer-scale dimensions, characterization becomes more difficult. Devices incorporating complex three-dimensional geometry and materials with diverse physical properties contribute to characterization difficulty.
In response to these challenges, more complex optical tools have been developed. Measurements are performed over a large ranges of several machine parameters (e.g., wavelength, azimuth and angle of incidence, etc.), and often simultaneously. As a result, the measurement time, computation time, and the overall time to generate reliable results, including measurement recipes, increases significantly.
In addition, existing model based metrology methods typically include a series of steps to model and then measure structure parameters. Typically, measurement data (e.g., DOE spectra) is collected from a particular metrology target. An accurate model of the optical system, dispersion parameters, and geometric features is formulated. In addition, simulation approximations (e.g., slabbing, Rigorous Coupled Wave Analysis (RCWA), etc.) are performed to avoid introducing excessively large errors. Discretization and RCWA parameters are defined. A series of simulations, analysis, and regressions are performed to refine the geometric model and determine which model parameters to float. A library of synthetic spectra is generated. Finally, measurements are performed using the library or regression in real time with the geometric model.
Currently, models of device structures being measured are assembled by a user of a measurement modeling tool from primitive structural building blocks. These primitive structural building blocks are simple geometric shapes (e.g., square frusta) that are assembled together to approximate more complex structures. The primitive structural building blocks are sized by the user based on user input that specifies the shape details of each primitive structural building block. In one example, each primitive structural building block includes an integrated customization control panel where a user inputs specific parameters that determine the shape details. Similarly, primitive structural building blocks are joined together by constraints that are also manually entered by the user. For example, the user enters a constraint that ties a vertex of one primitive building block to a vertex of another building block. This allows the user to build models that represent a series of the actual device geometries when the size of one building block changes. User-defined constraints between primitive structural building blocks enable broad modeling flexibility. For example, the thicknesses or heights of different primitive structural building blocks can be constrained to a single parameter in multi-target measurement applications. Furthermore, primitive structural building blocks have simple geometric parameterizations which the user can constrain to application-specific parameters. For example, the sidewall angle of a resist line can be manually constrained to parameters representing the focus and dose of a lithography process.
Although models constructed from primitive structural building blocks offer a wide range of modeling flexibility and user control, the model building process becomes very complex and error prone when modeling complex device structures. A user needs to assemble primitive structural building blocks together accurately, ensure they are correctly constrained, and parameterize the model in a geometrically consistent manner. Accomplishing this is not an easy task, and users spend significant amounts of time ensuring that their models are correct. In many cases, users do not realize their models are inconsistent and incorrect because it is difficult to comprehend how all of the primitive structural building blocks change shape and location in parameter space. Specifically, it is very difficult to determine if models that are structurally consistent for a given set of parameter values remain structurally consistent for another set of parameter values.
FIG. 1A depicts twelve different primitive structural building blocks 11-22 assembled together to form an optical critical dimension (OCD) model 10 depicted in FIG. 1B. Each primitive structural building block is rectangular in shape. To construct OCD model 10 a user must manually define the desired dimensions, constraints, and independent parameters (e.g., parameters subject to variation) of the model. Models constructed based on primitive structural building blocks (i.e., basic shapes such as rectangles) typically require a large number of primitives, constraints, and independent parameters for which the user must define ranges of variation. This makes model-building very complex and prone to user errors.
Furthermore, model complexity makes it difficult for one user to understand models built by another. The user needs to be able to understand the intent of the original model owner and this becomes increasingly challenging as the number of primitive structural building blocks, constraints, and independent parameters increases. Consequently, transferring ownership of models (e.g., from applications engineers to process engineers) is a time consuming, difficult process. In many cases, the complexity of the models leads to frustration amongst colleagues, and in some cases, prevents the transfer process from ever being fully completed. In some examples, a user generates a new model from primitive structural building blocks to mimic a model generated by a colleague. In many cases the resulting model is slightly different, and therefore delivers slightly different results due to the non-commutative property of floating point operations on computers. In some other examples, a user surrenders or risks intellectual property by having another firm develop the model.
Optical metrology structures have in the past remained simple enough that new models are commonly designed for each project. However, with increasingly complicated models and less time per project, improved modeling methods and tools are desired.